Sessional Lecturer MHI2004H Human Factors and System Design in Health Care (eMHI)

University of Toronto; Toronto; On-site; CAD 80,000 - 100,000; Part time

Job summary

An esteemed Canadian university seeks a Sessional Lecturer to teach a graduate course in health informatics for winter 2026. Candidates must have a PhD or Master's degree and relevant teaching experience. Responsibilities include course design and student engagement. Competitive salary based on experience. Applications via email by October 24, 2025.

Qualifications

• PhD or Master's level education with recent experience in clinical and health informatics.
• Robust understanding of clinical/clinician work processes influenced by health informatics.
• Past teaching experience related to health informatics, preferably at the graduate level.
• Prior experience in curriculum development and adult teaching methods.
• Comfortable with electronic teaching tools.

Responsibilities

• Course instructor for a professional graduate course.
• Responsible for course design and student outcome assessment.
• Must be accessible to students outside of classroom hours.

Skills

Clinical and health informatics experience

Understanding of clinical/clinician work processes

Teaching experience in health informatics

Curriculum development experience

Familiarity with electronic teaching tools

Education

PhD or Master's degree

Tools

Learning Management Systems (e.g., Canvas)

PowerPoint

Online collaboration tools

Job description

Overview

CUPE Local 3902 (Unit 3) Job Posting

Sessional Lecturer Position

Posting Date: October 3, 2025

Program: Executive Master of Health Informatics (eMHI)

Sessional Dates of Appointment: Winter 2026, January to April

Course Title: MHI2004H-S: Human Factors & System Design in Healthcare

Details

Course Description:

This course examines how human factors informs the design, delivery, and improvement of healthcare services in complex socio-technical systems. Rather than centering on individual apps or devices, the course focuses on end-to-end services across touchpoints, channels, and organizations. Students will learn service design methods that align clinical, operational, digital, and policy elements to deliver safe, effective, and equitable care.

Topics include human cognition and behavior in service contexts, journey mapping, service blueprinting, workflow and capacity analysis, and change leadership for scaling new models of care. The course explores how data and AI coordinate people, processes, and technology to support behavior change, precision care pathways, and continuous service improvement. Case studies span virtual, community, acute, and primary care settings. Studio activities give students hands-on practice with field research, co-design, service prototyping, and evaluation of service quality, safety, and experience.

Learning Outcomes

By the end of the course, students will be able to:

• Explain adoption of digital health within complex service ecosystems and socio-technical environments.
• Apply human factors principles to service contexts including cognition, workload, teamwork, communication, and environment.
• Use core service design methods: stakeholder and ecosystem mapping, patient and staff journey maps, service blueprints, and scenario storyboards.
• Clinical and administrative workflow analysis, including handoffs, failure points, risks, and opportunities for standard work.
• Design and prototype service concepts using low-fidelity pilots, role play, wizard-of-oz trials, and simulation to de-risk delivery.
• Evaluate services using usability, safety, quality, access, equity, and experience measures, including PREMs and operational KPIs such as cycle time and throughput.
• Assess cultural readiness and change impacts across clinical, operational, and governance layers, and plan communications, training, and service recovery.
• Integrate digital tools into services in a way that aligns frontstage experiences with backstage processes, data flows, and policies.
• Determine strategies for scaling and sustaining services, including capacity management, queueing considerations, and cross-site coordination.
• Apply behavior change and decision support concepts, including how AI can route work, reduce cognitive load, personalize care, and support continuous learning.

Learning Activities

• Field research and contextual inquiry in healthcare settings
• Journey mapping and service blueprinting workshops
• Clinical workflow and capacity analysis
• Co-design sessions with patients, clinicians, and administrators
• Rapid service prototyping and small-scale pilots
• Evaluation planning for safety, experience, equity, and value
• Change leadership and stakeholder engagement planning

Qualifications

• A PhD or Masters level education with recent experience in clinical and health informatics, preferably in the areas of ICT adoption, implementation, and evaluation
• A robust understanding of clinical/clinician work processes, as influenced by health informatics and related technology
• Past teaching experience related to health informatics, preferably at the graduate level
• Prior experience in curriculum development and adult teaching-learning methods
• Comfortable with electronic teaching tools such as Learning Management Systems (e.g., Canvas), PowerPoint, as well as online collaboration tools (Blogs, Wikis, Discussion Boards, Webinars, or Video-conferencing)

Class schedule: Weekly

Estimated enrolment: 50

Estimated TA support: based on enrolment - None

Duties

• Course instructor for a professional graduate course using competency-based learning and assessment methods.
• Responsible for course design and assessment of student outcomes. Must be accessible to students outside of classroom hours.

Salary

Commensurate with experience

How to submit an application

Please send your CV and cover letter, outlining additional value you will bring to teaching the course via e-mail to [e-mail].

Closing Date

Closing Date: October 24, 2025

This job is posted in accordance with the CUPE 3902 Unit 3 Collective Agreement.

It is understood that some announcements of vacancies are tentative, pending final course determinations and enrolment. Should rates stipulated in the collective agreement vary from rates stated in this posting, the rates stated in the collective agreement shall prevail.

Preference in hiring is given to qualified individuals advanced to the rank of Sessional Lecturer II or Sessional Lecturer III in accordance with Article 14:12 of the CUPE 3902 Unit 3 collective agreement.

Please note: Undergraduate or graduate students and postdoctoral fellows of the University of Toronto are covered by the CUPE 3902 Unit 1 collective agreement rather than the Unit 3 collective agreement, and should not apply for positions posted under the Unit 3 collective agreement.